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The virtue of complexity in machine learning portfolios

Kelly, Bryan T.; Malamud, Semyon; Zhou, Kangying - 2021

Contrary to conventional wisdom in nance, return prediction R2 and optimal portfolio Sharpe ratio generally increase with model parameterization, even when minimal regularization is used. We theoretically characterize the behavior of return prediction models in the high complexity regime, i.e....

Persistent link: https://www.econbiz.de/10012800453

Principal portfolios

Kelly, Bryan T.; Malamud, Semyon; Pedersen, Lasse Heje - 2020 - This version: June 8, 2020

We propose a new asset-pricing framework in which all securities' signals are used to predict each individual return. While the literature focuses on each security's own- signal predictability, assuming an equal strength across securities, our framework is flexible and includes...

Persistent link: https://www.econbiz.de/10012271188

Universal portfolio shrinkage

Kelly, Bryan T.; Malamud, Semyon; Pourmohammadi, Mohammad; … - 2023

Persistent link: https://www.econbiz.de/10014483260

Narrative Asset Pricing : Interpretable Systematic Risk Factors from News Text

Bybee, Leland; Kelly, Bryan T.; Su, Yinan - 2021

We seek fundamental risks from news text. Conceptually, news is closely related to the idea of systematic risk, in … drives the current pricing kernel. This paper demonstrates a way to extract a parsimonious set of risk factors and eventually … attention allocated to different news narratives. As a result, the risk factors attain clear text-based interpretability as well …

Persistent link: https://www.econbiz.de/10013217295

Large (and deep) factor models

Kelly, Bryan T.; Kuznetsov, Boris; Malamud, Semyon; Xu, … - 2024

Persistent link: https://www.econbiz.de/10014483267